Translating Big Data to Clinical Outcomes in Anxiety: Potential for Multimodal Integration

Authors

Parmis Khosravi
André Zugman
Paia Amelio
Anderson M. Winkler, The University of Texas Rio Grande ValleyFollow
Daniel S. Pine

Document Type

Article

Publication Date

12-5-2022

Abstract

Purpose of the Review

This review describes approaches to research on anxiety that attempt to link neural correlates to treatment response and novel therapies. The review emphasizes pediatric anxiety disorders since most anxiety disorders begin before adulthood.

Recent Findings

Recent literature illustrates how current treatments for anxiety manifest diverse relations with a range of neural markers. While some studies demonstrate post-treatment normalization of markers in anxious individuals, others find persistence of group differences. For other markers, which show no pretreatment association with anxiety, the markers nevertheless distinguish treatment-responders from non-responders. Heightened error related negativity represents the risk marker discussed in the most depth; however, limitations in measures related to error responding necessitate multimodal and big-data approaches.

Summary

Single risk markers show limits as correlates of treatment response. Large-scale, multimodal data analyzed with predictive models may illuminate additional risk markers related to anxiety disorder treatment outcomes. Such work may identify novel targets and eventually guide improvements in treatment response/outcomes.

Comments

Original published version available at https://rdcu.be/c4agd

Recommended Citation

Khosravi, P., Zugman, A., Amelio, P., Winkler, A. M., & Pine, D. S. (2022). Translating Big Data to Clinical Outcomes in Anxiety: Potential for Multimodal Integration. Current psychiatry reports, 24(12), 841-851. https://doi.org/10.1007/s11920-022-01385-6

Publication Title

Curr Psychiatry Rep

DOI

10.1007/s11920-022-01385-6

Academic Level

faculty

Mentor/PI Department

Office of Human Genetics